48 lines
1.6 KiB
Markdown
48 lines
1.6 KiB
Markdown
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---
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language: en
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license: other
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library_name: transformers
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tags:
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- chat
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- conversational
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- distillation
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- reasoning
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- code
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- chichu
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base_model: Qwen/Qwen2.5-0.5B-Instruct
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pipeline_tag: text-generation
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---
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# Chichu 2.0 500M Instruct 🐱
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A 500 million parameter language model fine-tuned from Qwen2.5-0.5B-Instruct on the
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[r0b0tlab/qwen3.8-max-glm5.2-kimi-k3-distillation](https://huggingface.co/datasets/r0b0tlab/qwen3.8-max-glm5.2-kimi-k3-distillation)
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dataset — a multi-teacher distillation corpus covering math, code, reasoning, and instructions.
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Named after Chichu the cat. 🐱
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## Model Details
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- **Base model:** Qwen/Qwen2.5-0.5B-Instruct
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- **Parameters:** 494M (2.16M LoRA adapters trained)
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- **Training:** LoRA fine-tuning (rank=16, alpha=32)
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- **Context length:** 32,768 tokens
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model = AutoModelForCausalLM.from_pretrained("Sebastianpro88/Chichu-2.0-500M-Instruct", torch_dtype=torch.float16, device_map="cpu")
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tokenizer = AutoTokenizer.from_pretrained("Sebastianpro88/Chichu-2.0-500M-Instruct")
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messages = [
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{"role": "system", "content": "You are Chichu 2.0, a language model named after Chichu the cat."},
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{"role": "user", "content": "What is your name?"}
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]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(text, return_tensors="pt")
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out = model.generate(**inputs, max_new_tokens=50)
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print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
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# "My name is Chichu 2.0."
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```
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